Trickey, A., Van Sighem, A., Stover, J., Abgrall, S., Grabar, S.,Bonnet, F., Berenguer, J., Wyen, C., Casabona, J., D'ArminioMonforte, A., Cavassini, M., Del Amo, J., Zangerle, R., Gill, M. J.,Obel, N., Sterne, J. A. C., & May, M. T. (2019). Parameter estimatesfor trends and patterns of excess mortality among persons onantiretroviral therapy in high-income European settings. AIDS, 33,S271-S281. https://doi.org/10.1097/QAD.0000000000002387

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Parameter estimates for trends and patterns of excess mortality among persons on ART in high-

income European settings

Short title: Excess mortality in the ART-CC

Words: 3714/3500

Adam TRICKEY - Population Health Sciences, University of Bristol, Bristol, UK

Ard VAN SIGHEM - Stichting HIV Monitoring, Amsterdam, The Netherlands

John STOVER - Avenir Health, Glastonbury, Connecticut

Sophie ABGRALL - Department of Internal Medicine, Antoine Béclère Hospital, Clamart, France;

University of Paris Saclay, Paris-Sud University, UVSQ, Le Kremlin-Bicêtre, France; CESP INSERM

U1018, Le Kremlin-Bicêtre, France

Sophie GRABAR - Sorbonne Université, INSERM, Institut Pierre Louis d’épidemiologie et de Santé

Publique (IPLESP), Unité de Biostatistique et d’épidémiologie Groupe hospitalier Cochin Broca Hôtel-

Dieu, Assistance Publique Hôpitaux de Paris (AP-HP), and Université Paris Descartes, Sorbonne Paris

Cité, Paris, France

Fabrice BONNET - University of Bordeaux, ISPED, INSERM U1219 and CHU de Bordeaux, F-33000,

Bordeaux, France

Juan BERENGUER - Hospital General Universitario Gregorio Marañón, Instituto de Investigación

Sanitaria Gregorio Marañón (IiSGM), Madrid, Spain

Christoph WYEN – First Department of Internal Medicine, University of Cologne, Cologne, Germany

Jordi CASABONA – CEEISCAT/Agència de Salut Pública de Catalunya, Campus Can Ruti and CIBERESP,

Badalona, Catalonia, Spain

Antonella D’ARMINIO MONFORTE - Clinic of Infectious and Tropical Diseases, Department of Health

Sciences, ASST Santi Paolo e Carlo, University of Milan, Milan, Italy

Matthias CAVASSINI - Service of Infectious Diseases, Lausanne University Hospital and University of

Lausanne, Lausanne, Switzerland

Julia DEL AMO - National Epidemiology Center, Carlos III Health Institute, Madrid, Spain and National

Plan on AIDS, Ministry of Health, Madrid, Spain

Robert ZANGERLE - Innsbruck Medical University, Austria

M John GILL - Division of Infectious Diseases, University of Calgary, Calgary, Canada

Niels OBEL - Department of Infectious Diseases, Copenhagen University Hospital, Rigshospitalet,

Denmark

Jonathan AC STERNE - Population Health Sciences, University of Bristol, Bristol, UK

Margaret T MAY - Population Health Sciences, University of Bristol, Bristol, UK

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Corresponding author: Adam Trickey, adam.trickey@bristol.ac.uk, Oakfield House, Population

Health Sciences, University of Bristol, Bristol, UK, BS8 2BN

ART-CC cohorts included: AHIVCOS, AQUITAINE, ATHENA, Co-RIS, DHCS, FHDH, ICONA, PISCIS, SHCS,

VACH – the cause of death coding committee (Sophie Abgrall, John Gill, Juan Berenguer, and

Christoph Wyen) are also being included as co-authors, and John Stover representing

UNAIDS/Spectrum

Declaration of interests

The following members of the writing committee, or their institution, received fees from the

following entities for work unrelated to this paper. MJG has received personal fees as an ad-hoc

member of the Canadian HIV advisory boards of Gilead, Merck, and ViiV. The European Centre for

Disease Prevention and Control funded Stichting HIV Monitoring for work by AVS. All other members

of the writing committee declare no competing interests. SA has received travel grant from Gilead

and ViiV and is member of Janssen-Cilag HIV board. JB has received research grants from AbbVie,

Gilead, Merck, and ViiV; as well as personal fees from AbbVie, Gilead, Janssen, Merck, and ViiV. FB

has served as a speaker for BMS, Gilead, MSD, and ViiV Healthcare, a consultant for Pierre Fabre and

ViiV Healthcare, and has received research funding from Gilead and Janssen.

Funding

This work was supported by the UK Medical Research Council (MRC; grant number MR/J002380/1)

and the UK Department for International Development (DFID) under the MRC/DFID Concordat

agreement and is also part of the EDCTP2 programme supported by the European Union. The ART-

CC is funded by the US National Institute on Alcohol Abuse and Alcoholism (U01-AA026209). JACS is

funded by National Institute for Health Research Senior Investigator award NF-SI-0611-10168. Data

from 11 European cohorts were pooled within COHERE in EuroCoord. COHERE receives funding from

the European Union Seventh Framework Programme (FP7/2007-2013) under EuroCoord grant

agreement number 260694. Sources of funding of individual cohorts include the Agence Nationale

de Recherche sur le SIDA et les hépatites virales (ANRS), the Institut National de la Santé et de la

Recherche Médicale (INSERM), the French, Italian, and Spanish Ministries of Health, the Swiss

National Science Foundation (grant 33CS30_134277), the Ministry of Science and Innovation and the

Spanish Network for AIDS Research (RIS; ISCIII-RETIC RD06/006), the Stichting HIV Monitoring, the

European Commission (EuroCoord grant 260694), and unrestricted grants from Abbott, Gilead,

Tibotec-Upjohn, ViiV Healthcare, MSD, GlaxoSmithKline, Pfizer, Bristol-Myers Squibb, Roche, and

Boehringer Ingelheim. The Danish HIV Cohort Study is funded by the Preben and Anne Simonsens

Foundation.

mailto:adam.trickey@bristol.ac.uk

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Abstract 249/250

Introduction: HIV cohort data from high-income European countries were compared to the UNAIDS

Spectrum modelling parameters for these same countries to validate mortality rates and excess

mortality estimates for people living with HIV (PLHIV) on antiretroviral therapy (ART).

Methods: Data from 2000-2015 were analysed from the Antiretroviral Therapy Cohort Collaboration

(ART-CC) for Austria, Denmark, France, Italy, the Netherlands, Spain, and Switzerland. Flexible

parametric models were used to compare All-cause mortality rates in the ART-CC and Spectrum. The

percentage of AIDS-related deaths and excess mortality (both are the same within Spectrum) were

compared, with excess mortality defined as that in excess of the general population mortality.

Results: Analyses included 94026 PLHIV with 585784 person-years of follow-up, from which there

were 5515 deaths. All-cause annual mortality rates in Spectrum for 2000-2003 were 0.0121,

reducing to 0.0078 in 2012-2015, whilst the ART-CC's corresponding annual mortality rates were

0.0151 (95% confidence interval [95%CI]: 0.0130-0.0171) reducing to 0.0049 (95%CI: 0.0039-0.0060).

The percentage of AIDS-related deaths in Spectrum was 74.7% in 2000-2003, dropping to 43.6% in

2012-15. In the ART-CC, AIDS-related mortality comprised 45.3% (95%CI: 38.4%-52.9%) of mortality

in 2000-2003 and 26.7% (95%CI: 19.0%, 46.0%) between 2012-2015. Excess mortality in the ART-CC

was broadly similar to the Spectrum estimates, dropping from 75.3% (95%CI: 60.3%-95.2%) in 2000-

2003 to 30.7% (95%CI: 25.5%-63.7%) in 2012-2015.

Conclusions: All-cause mortality assumptions for PLHIV on ART in high-income European settings

should be adjusted in Spectrum to be higher in 2000-2003 and decline more quickly to levels

currently captured for recent years.

Keywords: HIV, AIDS, cause-specific, death, UNAIDS, cohort, duration

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Introduction

Mortality rates among people living with HIV (PLHIV) taking antiretroviral therapy (ART) have

declined since the millennium in high-income European countries[1, 2] because of increasingly

effective ART regimens that are easy to take and have minimal side effects, as well as other

improvements in HIV care[3]. Changes to HIV treatment guidelines, which now recommend that

PLHIV start ART as soon as possible after diagnosis regardless of their CD4 count, will also have

impacted mortality in recent years[4]. Many studies have shown higher mortality during the first year

of ART than subsequently[5-7], but this early mortality has declined in recent years because fewer

PLHIV start ART with severe immunodeficiency[1]. Causes of death among PLHIV on ART have also

changed, with a smaller proportion of AIDS-related deaths[2]. As the HIV-positive population ages the

proportion of deaths from causes associated with ageing, such as cancer and cardiovascular disease,

has increased[2, 8].

Spectrum (www.unaids.org/en/dataanalysis/datatools/spectrum-epp) is a comprehensive set of

models of the HIV epidemics in countries around the world that is used by the United Nations

Programme on HIV/AIDS (UNAIDS) to produce country-specific estimates of all-cause and AIDS-

related mortality in PLHIV[9]. Spectrum model outputs are used by governments, researchers, and

policy-makers globally, so it is important that they are as accurate as possible. To test the accuracy

of the Spectrum mortality estimates, UNAIDS sought to compare methods, parameters and outputs

of the Spectrum models with those of observational HIV cohorts with good cause of death

classifications.

We used data from the Antiretroviral Therapy Cohort Collaboration (ART-CC)[10] of clinical HIV

cohorts in seven high-income European countries to validate Spectrum model parameters and

outputs. Analyses compared excess mortality estimates for PLHIV on ART in ART-CC with those from

Spectrum. The secondary aims were to: 1) investigate the association between duration of ART and

mortality; 2) compare mortality rates by calendar period in the ART-CC with trends estimated using

Spectrum; 3) compare the proportion of deaths on ART that are due to AIDS in the ART-CC with

Spectrum; and 4) provide updated mortality rates to parameterise Spectrum for high-income

European countries and enable more accurate estimates of excess deaths due to AIDS.

Methods

ART-CC data

The ART-CC combines data from HIV cohorts in Europe and North America. Eligible patients are HIV-

1 positive, aged ≥16 years, and started ART on ≥3 drugs without having previously taken

antiretrovirals. All contributing cohorts have been approved to use their data for research by

institutional review boards or ethics committees. The cohorts use standardized methods of data

collection, with follow-up visits scheduled at least every six months. The dataset is described in

detail elsewhere (www.bristol.ac.uk/art-cc/)[10]. Only European ART-CC cohorts were included in this

study as the North American cohorts within NA-ACCORD are more representative of the US and

Canadian populations of PLHIV than those in the ART-CC[11]. The European cohorts included in this

study were those representative of national populations of PLHIV in their countries due to their

nationwide geographical coverage and because they contain a high percentage of the PLHIV on ART

in that country, eg. AHIVCOS contains over 85% of Austria’s PLHIV on ART[12]. The countries and

cohorts analysed were Austria (AHIVCOS), Denmark (DHK), France (ANRS C03 Aquitaine cohort,

ANRS CO4 French Hospital Database on HIV FHDH), Italy (ICONA), Netherlands (ATHENA), Spain (Co-

http://www.unaids.org/en/dataanalysis/datatools/spectrum-epp

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RIS, PISCIS, VACH), and Switzerland (SHCS). The ART-CC 2015 data update was used (ART-CC receives

data from its cohorts in cycles, for example the previous update was in 2013). The latest ART start

date in this analysis was 9th January 2015 and the latest follow-up date used was 31st December

2015.

Follow-up started when PLHIV started ART and ended at the earliest of death, loss to follow-up

(LTFU), or administrative censoring (a cohort-specific database closing date). If a patient’s last clinical

observation was more than a year before the cohort-specific database close date and they were not

known to have died, then they were considered lost to follow up. Alternative definitions of LTFU

using gaps of no contact of one and half years and two years were investigated. Data on follow-up

between 2000-2015 were analysed, for comparability with Spectrum.

Coding causes of death

Data on causes of death were obtained through either linkage with Vital Statistics agencies and

hospitals or through active follow-up and physician report, depending on the cohort. An adapted

version of the Cause of Death (CoDe) project protocol (www.cphiv.dk/CoDe.aspx) was used to

classify causes of death[13]. When ICD-9 or ICD-10 codes or free-text information were available,

causes of death were classified by a computer algorithm and a clinician. When either ICD-9 or ICD-10

codes or free-text information were unavailable, then each death was independently classified by

two clinicians. Panel discussion was used to resolve disagreements between clinicians and/or the

algorithm. Deaths were classified as AIDS-related if a serious AIDS defining condition had been

recorded a year prior to the death, and/or a low CD4 count (

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estimated overall and by country. Annual mortality rates were calculated by dividing the number of

deaths by the number of PLHIV alive at the midpoint of that year, presented as rates per person.

LTFU in Spectrum only affects the distribution of ART patients by time on ART as the input to

Spectrum is the number of PLHIV on ART. High rates of LTFU mean that more people need to start

ART to match the program reports of numbers on ART. As on-ART mortality is higher in the first year

of treatment, higher LTFU rates are associated with higher overall ART-related mortality.

Analyses

ART-CC data were reshaped to separate each calendar year of follow-up for each individual person.

Age (grouped as 16-24, 25-34, 35-44, and ≥45 years) was updated for each calendar year of follow-

up for each person. The duration on ART for each person was updated for each calendar year and

categorised firstly as 0-6, 7-11, and ≥12 months, then secondly as 0-

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suggested that they were not from AIDS, although it was not sufficient to identify a classifiable cause

of death.

Excess mortality was calculated relative to the UN’s sex and age-matched general population

mortality rates. The percentage of excess deaths in ART-CC was calculated by dividing the excess

mortality rate by the overall mortality rate, with Bayesian 95% credibility intervals accounting for

parameter uncertainty calculated using Winbugs 1.4.3. All other analyses used Stata version 15.1.

Results

The ART-CC dataset contained 94026 PLHIV: 25534 (27.2%) female and 9357 (10.0%) with IDU

transmission). During 585784 person-years of follow-up after 2000 there were 5515 deaths. At the

time of starting ART, median age was 37 years (IQR 31-45), median CD4 count was 252 cells/μL (IQR

126-377), median HIV-1 viral load was 63160 (IQR 11900-199800) copies/mL and 17127 (18.2%)

PLHIV had been diagnosed with AIDS. When LTFU was defined as no clinical follow-up a year before

the administrative censoring date, 20763 (22.1%) PLHIV were considered lost, reducing to 15513

(16.5%) and 6850 (7.3%) for gaps of 1.5 and 2 years, respectively.

ART duration

When duration of ART was categorised as 0-6, 7-11, and ≥12 months, mortality rates were highest

during the first 0-6 months on ART (figure 1a and supplementary table 1). For a male with non-IDU

transmission, aged 16-24, and baseline CD4 count 100-199 cells/μL in their first 0-6 months on ART,

the annual mortality rate in 2000-2003 was 0.017 (95%CI 0.011, 0.022) per-person, declining to

0.006 (0.004, 0.008) between 2012 and 2015. The adjusted mortality IRRs comparing 7-12 and ≥12

with 0-6 months ART duration were 0.52 (95%CI 0.47-0.59) and 0.35 (0.33-0.38) respectively. When

duration was categorised as 0-1, 1-2, 2-3, 3-4, and ≥4 years, mortality rates were highest in the first

year of ART (figure 1b and supplementary table 1). For a non-IDU male, aged 16-24 years with

baseline CD4 count 100-199 cells/μL in their first year on ART, the 2000-2003 annual mortality rate

was 0.013 (0.009, 0.017), declining to 0.005 (0.003, 0.006) in 2012-2015. The adjusted IRRs

compared with the first year of ART were: 0.52 (95%CI 0.47-0.57) for 1-2 years, 0.49 (0.44-0.54) for

2-3 years, 0.43 (0.38-0.47) for 3-4 years, and 0.44 (0.41, 0.48) for ≥4 years on ART.

Calendar year period

Figure 2 shows crude and selected country-specific comparisons of mortality rates between the ART-

CC and Spectrum estimates, by calendar year period: supplementary table 2 provides further detail.

For Europe overall and all the countries analysed except Austria and the Netherlands, the ART-CC

crude mortality rates were higher than the Spectrum estimates in the 2000-03 period but lower by

2012-2015. This indicates that the Spectrum mortality rates are not declining as quickly as those in

the ART-CC. France and Switzerland had the lowest mortality rates in ART-CC data, whilst Austria and

Denmark had the highest. The ART-CC analyses suggested that in some countries declines in

mortality were less rapid in more recent years: this was not apparent in the Spectrum modelling.

Mortality rates appeared to decline sooner in France and Switzerland than in the other countries

analysed. Using cause of death coding, estimated rates of AIDS-related mortality, in ART-CC were

0.0049 in 2000-03, 0.0028 in 2004-07, 0.0020 in 2008-11, and 0.0007 in 2012-15: much lower than in

Spectrum (0.0091, 0.0074, 0.0054, and 0.0032 in the same periods). However, estimated excess

mortality rates in ART-CC were more similar to those in Spectrum 0.0115, 0.0082, 0.0045, and

0.0021 in the same periods), although excess mortality declined more across calendar periods in

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ART-CC than in Spectrum. Supplementary table 3 shows that declines in mortality rates over

calendar years were less pronounced among PLHIV starting ART with higher than lower CD4 counts.

Causes of death

Table 1 shows the distribution of specific causes of death in the ART-CC by calendar year period. The

percentage of deaths in ART-CC that were classified as AIDS-related declined from 36% in 2000-03 to

27% in 2004-07, 25% in 2008-11, and 13% in 2012-2015. However, the percentage classified as

unknown increased from 16% in 2000-03 to 19% in 2004-07, 20% in 2008-11, and 39% in 2012-15.

Supplementary table 4 shows lower and upper bounds, and middle estimate of the percentage of

AIDS-related deaths, based on different assumptions about the unclassified/unknown deaths. Figure

3 shows a comparison of the percentage of deaths due to AIDS between Spectrum and the ART-CC.

Supplementary table 5 gives country-specific estimates and confidence intervals. The estimated

percentage of AIDS deaths was lower in ART-CC than in the Spectrum estimates for all calendar year

periods, even after assuming that all unknown/unclassifiable deaths were due to AIDS. For 2000-

2011 the percentage of AIDS deaths was lower in ART-CC than in the Spectrum estimates for each

country analysed, even after assuming that all unknown/unclassifiable deaths in ART-CC were due to

AIDS. For 2012-2015 the percentage of AIDS deaths was lower in ART-CC than in the Spectrum

estimates for each country, but this was no longer the case for Denmark, France, Italy, and the

Netherlands when assuming all unknown/unclassifiable deaths in ART-CC were AIDS-related.

Estimated percentage excess mortality in ART-CC (75.3% in 2000-03, 66.4% in 2004-07, 52.4% in

2008-11, 30.7% in 2012-15) was similar to Spectrum (74.7%, 68.4%, 59.1% and 43.6% in the same

calendar periods) except for 2012-15.

Discussion

Overall, mortality rates in Spectrum appeared to have been under-estimated for the early years of

ART and over-estimated for the later years, compared with the ART-CC data. Mortality rates were

much higher for PLHIV in the ART-CC during their first 0-6 months or first year on ART than

afterwards. Higher mortality rates at the beginning of ART are likely due to PLHIV presenting late for

treatment with compromised immune systems. Correspondingly, the decline in mortality over

calendar years is less pronounced for PLHIV presenting with higher CD4 counts. In both the ART-CC

and Spectrum the percentage of deaths due to AIDS decreased over the calendar years. The

percentage of deaths due to AIDS appeared to be over-estimated in Spectrum because it was higher

than in the ART-CC, even when assuming all unknown/unclassifiable deaths in the ART-CC were due

to AIDS. However, the percentage of deaths among PLHIV in the ART-CC that were in excess of the

general population mortality was broadly similar to that in Spectrum.

These comparisons between Spectrum and the ART-CC draw attention to how excess mortality is

estimated among PLHIV on ART in high-income settings. Extra mortality can manifest as deaths due

to AIDS but can also be due to other causes that more common among PLHIV than in the general

population such as increased inflammation, hepatitis C virus coinfection, smoking, and IDU[17-21].

Comparing mortality rates between Spectrum and the ART-CC indicates that Spectrum’s

assumptions need to be adjusted to reflect higher mortality rates in 2000-03 and lower rates in more

recent years. Adjustments should account for mortality rates declining more steeply than currently

assumed. The decrease in mortality rates observed in the ART-CC occurred earlier in some countries

than others, possibly due to earlier introduction of guidelines and policies for treating HIV[22].

Another explanation could be variation in the demographic characteristics of PLHIV across the

9

countries[8]. Mortality rate decreases observed in ART-CC were not linear, for example the decrease

for Switzerland appeared to level off over time and so may have reached a steady state with little

room for further improvement in the survival of PLHIV. The increase in the proportion of deaths in

the ART-CC coded as unknown/unclassifiable in 2012-2015 compared to earlier years could be due

to various reasons, one being that for some cohorts these later deaths had not been linked to cause

of death information at the time of the data update.

Comparison with literature

A European multi-cohort study by the D:A:D collaboration, containing some of the same cohorts as

ART-CC, found the percentage of deaths due to AIDS to be 29% between 1999 and 2011[2]. This is

lower than the percentage found in the European ART-CC cohorts (43% in 2000-03 dropping to 31%

in 2008-11), but not too dissimilar and could be explained by differences between the cohorts

analysed. A Public Health England study found the percentage of deaths due to AIDS to be 58%

between 1997 and 2012[23], although this study also included PLHIV not on ART, possibly explaining

the higher percentage. Studies in other high-income countries also found similar percentage of

deaths due to AIDS, such as 40% in the Australian AHOD cohort between 1999-2004[24], similar to the

ART-CC’s 43% for 2000-03. The BC-CfE study in Vancouver, Canada, found the percentage of deaths

due to AIDS to be 73% in 2005, falling to 20% in 2013[25]. The 73% is much higher than the ART-CC’s

comparative 34% for 2004-2007, possibly due to different patient mix and guidelines. However, BC-

CfE’s 20% figure is almost identical to the ART-CC’s 19% seen between 2012-2015. There is

considerable evidence that, as shown in this study, mortality is highest during the first year of ART

and decreases afterwards[5-7].

Strengths and limitations

The HIV cohorts included in this analysis are a good representation of the PLHIV on ART in their

countries in terms of patient mix and due to large sample sizes[10]. The under-ascertainment of

deaths in the ART-CC due to LTFU may be a limitation, but registry linkages of deaths have been

done in most cohorts. A French study from 2000 identified a large underreporting of deaths in FHDH

itself, 62%, which dropped to 8% once other sources of mortality were accounted for[26]. A previous

ART-CC analysis from 2012 gives self-reported completeness of death ascertainment by cohort and

found that cohorts with lower completeness also had lower reported mortality rates[27]. LTFU in the

ART-CC could be due to PLHIV becoming sicker and not turning up to appointments, conversely, it

could be due to PLHIV being healthy enough to move to another clinic, cohort, or country[28]. Despite

the percentage of AIDS-related deaths in the ART-CC being lower than in Spectrum, the cause of

death coding system in the ART-CC may over-estimate AIDS-related deaths because of the use of

indirect evidence of AIDS, such as low CD4 counts within the year before death[29]. Additionally, how

excess mortality is defined can make comparisons of mortality rates between the ART-CC and

Spectrum difficult. Differences between Spectrum and the ART-CC could be methodological or data-

driven, which is also hard to ascertain. For comparability with Spectrum, important variables were

not adjusted for, so assumptions were made that the characteristics of PLHIV in the ART-CC and

Spectrum were similar, which may not be the case. For other analyses some variables, such as

geographical origin, hepatitis C status, and socio-demographic indicators, were not adjusted for as

they were unavailable for many PLHIV.

Implications

The findings of this study indicate that the assumptions around all-cause mortality for PLHIV on ART

in high-income European settings should be adjusted in Spectrum. Mortality rates for PLHIV on ART

10

were higher in 2000-03 and declined more quickly than captured by Spectrum. Although when

calculating excess mortality in the ART-CC the mortality rates are broadly similar to those in

Spectrum, the percentage of deaths due to AIDS among PLHIV in the ART-CC is much lower. These

lower percentages seen in the ART-CC have also been recorded in other studies in high-income

countries[2, 24, 25]. With increasingly effective ART, much of the excess mortality among PLHIV is due

to other factors such as higher levels of smoking, IDU, inflammatory markers, and hepatitis co-

infection than in the general population[19-21]. This possibly indicates excess mortality in Spectrum

should be broken into two categories: AIDS-related excess mortality, and non-AIDS-related excess

mortality. Additionally, this study gives further information about the effect of duration of ART on

mortality rates and cause-specific mortality among PLHIV on ART in high-income European

countries. These findings are likely generalisable to other high-income settings. The new rates

reported here in supplementary table 1 (model 1) have been incorporated into Spectrum for the

2019 round of UNAIDS HIV estimates.

Acknowledgments

We thank all PLHIV, doctors, and study nurses associated with the participating cohort studies.

11

Table 1: Distribution of cause-of-death in the ART-CC by calendar year period

Calendar year period Cause of death 2000-2003 2004-2007 2008-2011 2012-2015

AIDS 496 (36%) 454 (27%) 429 (25%) 90 (13%) Cardiovascular 52 (4%) 94 (6%) 85 (5%) 30 (4%) Liver 98 (7%) 111 (7%) 139 (8%) 50 (7%) Non-AIDS infection 64 (5%) 107 (6%) 84 (5%) 18 (3%) Cancer (non-AIDS or liver) 128 (9%) 218 (13%) 281 (16%) 103 (15%) Other 200 (14%) 242 (14%) 219 (13%) 83 (12%) Respiratory 14 (1%) 28 (2%) 30 (2%) 26 (4%) Substance abuse 47 (3%) 37 (2%) 50 (3%) 10 (1%) Unknown/unclassifiable 225 (16%) 331 (19%) 337 (20%) 274 (39%) Unnatural 56 (4%) 78 (5%) 70 (4%) 26 (4%)

Total 1380 (100%) 1700 (100%) 1725 (100%) 710 (100%)

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Figure 1: ART-CC trends across calendar years in mortality rates by duration of ART for a) 0-6, 7-12

months, and ≥1 years; and b) 0-1, 1-2, 2-3, 3-4, and ≥4 years

*Presented for a male with non-IDU transmission, aged 35-44, with baseline CD4 of 250-349 cells/μL

13

Figure 2: Comparison of ART-CC mortality rates (crude and selected*) against Spectrum model

inputs

*A non-IDU male, aged 35-44, with a CD4 count at ART start of 100-199 cells/μL, who had been on

ART for 1-2 years.

14

Figure 3: Comparisons of the percentage of deaths due to AIDS in the ART-CC against Spectrum

model inputs, for separate European countries

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